This paper presents a literature synthesis of world-class AI innovation, big-data innovation, and biotech innovation efforts that seek to enhance AI-assisted predictions for personal or precision biomedicines. This paper uses a technique for a qualitative-quantitative literature synthesis for developing a so-called AI bio-Innovation (AIBI), which proposes a literature synthesis of state-of-the-art works on AI innovation for predicting chronic diseases and AI applications for antibiotic resistances, while incorporating big-data models for innovations. This paper asserts that AI applications would provoke a minimum of 40% improvement for AI-related applications to enhance AI-assisted drug innovation development. This paper asserts that AI can aid predictions with a precision accuracy of 93% for predictions of heart-related diseases; a minimum of 25% improvement of AI-related innovations for antibiotic resistances; and presents AI as a scientific catalyst for healthcare sustainability that continues to advance with AI-assisted precision medicines.